Quantitative Structure-Property Relationship Analysis Against Critical Micelle Concentration of Sulfonate-Based Surfactant Based on Semiempirical Zindo/1 Calculation

Iswanto, Ponco; Delsy, Eva Vaulina Yulistia; Setiawan, Ely; Putra, Fiandy Aminullah

doi:10.20884/1.jm.2019.14.2.467

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…Therefore, the best QSAR equation for 3-thiocyanate-1H-indoles was IC50 = -1.705 + 0.511(Delta) + 0.346(Dipol) + 18.287(qC9) -0.645(Log P) + 13.952(qC6), with n = 20; r = 0.814; r 2 = 0.662; SE = 1.044; Fcount/Ftable = 1.851; PRESS = 15.219. The correlation graph between predicted and experimental activities (IC50) shows a slope close to 1 (Table 10, Figure 2) [26], meaning that the resulting equation can provide a good level of prediction.…”

Section: Determination Of the Best Equation Model With Multiple Linea...mentioning

confidence: 82%

“…The analysis included the correlation coefficient (r), partition coefficient (r 2 ), standard deviation (SD), Fcount/Ftable, and PRESS on four test compounds. The chosen model was then analyzed using the Enter method for all 20 compounds derived from 3thiocyanate-1H-indoles to obtain the best QSAR equation [26]. This analysis was performed using the SPSS software.…”

Section: Determination Of the Qsar Equationmentioning

confidence: 99%

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Quantitative Structure-Activity Relationship of 3-Thiocyanate-1H-Indoles Derived Compounds as Antileukemia by AM1, PM3, and RM1 Methods

Iswanto,

Firdaus,

Dafaulhaq

et al. 2023

J. Kim. Sains Apl.

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Cancer is a disease with fatal consequences; thus, searching for innovative compounds with anticancer properties remains an active pursuit. One of the highly promising candidates is a compound derived from 3-thiocyanato-1H-indoles. However, the number of derivative compounds is currently limited. A quantitative structure and activity relationship (QSAR) study was conducted on derivate compounds 3-thiocyanato-1H-indoles to establish equations that predict the anticancer activity of more effective derivatives. This study aims to compare the effectiveness of the AM1 (Austin Model 1), PM3 (Parameterized Model 3), and RM1 (Recife Model 1) semiempirical methods, which are new techniques implemented in the Hyperchem version 8.0. Twenty experimental data were used, 16 derivatives of 3-thiocyanate-1H-indoles as regression compounds (fitting) and four derivates as test compounds. QSAR analysis was performed based on multiple linear regression calculations on 3-thiocyanate-1H-indoles derivative compounds by plotting IC50 (µM) as the dependent variable and descriptors as the independent variable. The best QSAR equation was obtained from the AM1 semiempirical calculation method with the following equation: IC50 = -1.705 + 0.511(Delta) + 0.346(Dipol) + 18.287(qC9) – 0.645(Log P) + 13.952(qC6), with n =20; r =0.814; r2 =0.662; The standard error (SE) =1.044; Fcount/Ftable =1.851; PRESS =15.219.

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Section: Determination Of the Best Equation Model With Multiple Linea...mentioning

confidence: 82%

Section: Determination Of the Qsar Equationmentioning

confidence: 99%

Quantitative Structure-Activity Relationship of 3-Thiocyanate-1H-Indoles Derived Compounds as Antileukemia by AM1, PM3, and RM1 Methods

Iswanto,

Firdaus,

Dafaulhaq

et al. 2023

J. Kim. Sains Apl.

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“…This technique known as quantitative structure-property relationships (QSPR) has already shown its predictive potential for a broad spectrum of properties. QSPR models are mathematical relationships that are defined by molecular descriptors between the molecular structure and the target property 14 . Several models such as multiple linear regressions (MLR) or artificial neural networks are used for developing the QSPR model 15 .…”

Section: Introductionmentioning

confidence: 99%

QSPR Models for Predicting Critical Micelle Concentration of Gemini Cationic Surfactants Combining Machine-Learning Methods and Molecular Descriptors

Setiawan¹,

Mudasir²,

Wijaya³

2020

Preprint

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A data set of 231 diverse gemini cationic surfactants has been developed to correlate the logarithm of critical micelle concentration (cmc) with the molecular structure using a quantitative structure-property relationship (QSPR) methods. The QSPR models were developed using the Online CHEmical Modeling environment (OCHEM). It provides several machine learning methods and molecular descriptors sets as a tool to build QSPR models. Molecular descriptors were calculated by eight different software packages including Dragon v6, OEstate and ALogPS, CDK, ISIDA Fragment, Chemaxon, Inductive Descriptor, SIRMS, and PyDescriptor. A total of 64 QSPR models were generated, and one consensus model developed by using a simple average of 13 top-ranked individual models. Based on the statistical coefficient of QSPR models, a consensus model was the best QSPR models. The model provided the highest R 2 = 0.95, q 2 = 0.95, RMSE = 0.16 and MAE = 0.11 for training set, and R 2 = 0.87, q 2 = 0.87, RMSE = 0.35 and MAE = 0.21 for test set. The model was freely available at https://ochem.eu/model/8425670 and can be used for estimation of cmc of new gemini cationic surfactants compound at the early steps of gemini cationic surfactants development.

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Generic QSPR study for predicting critical micelle concentration of gemini cationic surfactants using the online chemical modeling environment (OCHEM)

Setiawan

Wijaya

Mudasir

2021

4th International Seminar on Chemistry

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Quantitative Structure-Property Relationship Analysis Against Critical Micelle Concentration of Sulfonate-Based Surfactant Based on Semiempirical Zindo/1 Calculation

Cited by 6 publications

References 7 publications

Quantitative Structure-Activity Relationship of 3-Thiocyanate-1H-Indoles Derived Compounds as Antileukemia by AM1, PM3, and RM1 Methods

Quantitative Structure-Activity Relationship of 3-Thiocyanate-1H-Indoles Derived Compounds as Antileukemia by AM1, PM3, and RM1 Methods

QSPR Models for Predicting Critical Micelle Concentration of Gemini Cationic Surfactants Combining Machine-Learning Methods and Molecular Descriptors

Generic QSPR study for predicting critical micelle concentration of gemini cationic surfactants using the online chemical modeling environment (OCHEM)

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